Hierarchical Multi-Frequency Transform for Sequential Recommendation
Zhenyi Fan, Hongbin Zhang, Guangyu Lin, Lianglun Cheng, Zhuowei Wang, Chong Chen · 2024
Sequential Recommendation (SR) aims to understand user preferences by analyzing historical interactions with items. Recent approaches have shifted from the time domain to the frequency domain to potentially enhance preference modeling. While fast Fourier transform is a common choice for frequency transform, it may introduce issues like the Gibbs phenomenon, leading to potentially suboptimal model performance. To address this, we introduce discrete cosine transform into sequential recommendation and present a novel multi-frequency transformation sequential recommendation, named HMFTRec, within a hierarchical framework. Specifically, we develop a discrete cosine transform module base on channel attention. A hierarchical spectrum framework that combines Fourier and discrete cosine transforms is introduced to capture finer-grained frequency domain information and mitigate the Gibbs phenomenon to some extent. Furthermore, contrastive learning is employed to potentially enhance the quality of user embeddings learned from the frequency domain. Extensive experiments conducted on four widely recognized benchmark datasets demonstrate that our model significantly outperforms state-of-the-art approaches.